Bottom Line:
Whole transcriptome shotgun sequencing (RNA-Seq) is a useful tool for analyzing the transcriptome of a biological sample.With appropriate statistical and bioinformatic processing, this platform is capable of identifying significant differences in gene expression within the transcriptome and permits pathway and network analyses to determine how these genes interact biologically.We performed this study in order to illustrate a workflow for identifying interesting genes and processes that are regulated early in EMT and to determine their gene pathway/network relationships and regulation.

ABSTRACTWhole transcriptome shotgun sequencing (RNA-Seq) is a useful tool for analyzing the transcriptome of a biological sample. With appropriate statistical and bioinformatic processing, this platform is capable of identifying significant differences in gene expression within the transcriptome and permits pathway and network analyses to determine how these genes interact biologically. In this study, we examined gene expression in two lung adenocarcinoma cell lines (H358 and A459) that were treated with transforming growth factor-β (TGF-β) as a model for induction of the epithelial-to-mesenchymal transition (EMT), commonly associated with disease progression. We performed this study in order to illustrate a workflow for identifying interesting genes and processes that are regulated early in EMT and to determine their gene pathway/network relationships and regulation. With this, we identified 137 upregulated and 32 downregulated genes common to both cell lines after TGF-β treatment that represent components of multiple canonical pathways and biological networks associated with the induction of EMT. These findings were also verified against reposited Affymetrix U133a expression profiles from multiple trials examining metastatic progression in patient cohorts (n = 731 total) to further establish the clinical relevance and translational significance of the model system. Together, these findings help validate the relevance of the TGF-β model for the study of EMT and provide new insights into early events in EMT.

f1-cin-suppl.5-2014-129: Comparison of expression value distributions between samples. Average expression value was calculated in each cell line with or without TGF-β treatment (control), respectively.

Mentions:
After expression values were calculated from the RNA-seq results, a total of 54,607 candidates with unique Ensembl gene IDs were identified. When subjected to filtering with statistical (P ≤ 0.05) and fold change (≥2) thresholds, the H358 cell line was found to have a total of 1177 upregulated genes and 1108 downregulated genes, whereas the A549 cell line had 525 upregulated and 167 downregulated genes, all relative to the cell line controls (eg, no treatment). These general findings are illustrated in Figure 1 as “box and whisker” plots. In general, there were fewer downregulated genes that met our statistical and fold-change thresholds than those upregulated. In comparing the findings of unique genes modulated in each cell line, a total of 137 upregulated and 32 downregulated genes were shared between H358 and A549 cell lines with the TGF-β induction, as shown in Figure 2 as a Venn diagram. Table 1 shows the top 10 common upregulated and downregulated genes in the two cell lines after TGF-β treatment. The largest increases in gene expression in this model were SERPINE1 in the H358 cells, with a 344.39-fold change, and DOCK2 in the A549 cells, with a 103.29-fold change (Table 1). Conversely, we observed the most dramatic decreases in gene expression in this model to be MUC5B in the H358 cells, with a 64.65-fold change, and ST6GALNAC1 in the A549 cells, with a 118.58-fold change (Table 1). Supplementary Tables 1–4 provide additional examples of genes up- and downregulated in response to the TGF-β treatment, separated by cell line and direction of change. Many of these targets are typically associated with or considered hallmark biomarkers of the EMT thought to mechanistically underlie tumor metastasis in vivo.9,32

f1-cin-suppl.5-2014-129: Comparison of expression value distributions between samples. Average expression value was calculated in each cell line with or without TGF-β treatment (control), respectively.

Mentions:
After expression values were calculated from the RNA-seq results, a total of 54,607 candidates with unique Ensembl gene IDs were identified. When subjected to filtering with statistical (P ≤ 0.05) and fold change (≥2) thresholds, the H358 cell line was found to have a total of 1177 upregulated genes and 1108 downregulated genes, whereas the A549 cell line had 525 upregulated and 167 downregulated genes, all relative to the cell line controls (eg, no treatment). These general findings are illustrated in Figure 1 as “box and whisker” plots. In general, there were fewer downregulated genes that met our statistical and fold-change thresholds than those upregulated. In comparing the findings of unique genes modulated in each cell line, a total of 137 upregulated and 32 downregulated genes were shared between H358 and A549 cell lines with the TGF-β induction, as shown in Figure 2 as a Venn diagram. Table 1 shows the top 10 common upregulated and downregulated genes in the two cell lines after TGF-β treatment. The largest increases in gene expression in this model were SERPINE1 in the H358 cells, with a 344.39-fold change, and DOCK2 in the A549 cells, with a 103.29-fold change (Table 1). Conversely, we observed the most dramatic decreases in gene expression in this model to be MUC5B in the H358 cells, with a 64.65-fold change, and ST6GALNAC1 in the A549 cells, with a 118.58-fold change (Table 1). Supplementary Tables 1–4 provide additional examples of genes up- and downregulated in response to the TGF-β treatment, separated by cell line and direction of change. Many of these targets are typically associated with or considered hallmark biomarkers of the EMT thought to mechanistically underlie tumor metastasis in vivo.9,32

Bottom Line:
Whole transcriptome shotgun sequencing (RNA-Seq) is a useful tool for analyzing the transcriptome of a biological sample.With appropriate statistical and bioinformatic processing, this platform is capable of identifying significant differences in gene expression within the transcriptome and permits pathway and network analyses to determine how these genes interact biologically.We performed this study in order to illustrate a workflow for identifying interesting genes and processes that are regulated early in EMT and to determine their gene pathway/network relationships and regulation.

ABSTRACTWhole transcriptome shotgun sequencing (RNA-Seq) is a useful tool for analyzing the transcriptome of a biological sample. With appropriate statistical and bioinformatic processing, this platform is capable of identifying significant differences in gene expression within the transcriptome and permits pathway and network analyses to determine how these genes interact biologically. In this study, we examined gene expression in two lung adenocarcinoma cell lines (H358 and A459) that were treated with transforming growth factor-β (TGF-β) as a model for induction of the epithelial-to-mesenchymal transition (EMT), commonly associated with disease progression. We performed this study in order to illustrate a workflow for identifying interesting genes and processes that are regulated early in EMT and to determine their gene pathway/network relationships and regulation. With this, we identified 137 upregulated and 32 downregulated genes common to both cell lines after TGF-β treatment that represent components of multiple canonical pathways and biological networks associated with the induction of EMT. These findings were also verified against reposited Affymetrix U133a expression profiles from multiple trials examining metastatic progression in patient cohorts (n = 731 total) to further establish the clinical relevance and translational significance of the model system. Together, these findings help validate the relevance of the TGF-β model for the study of EMT and provide new insights into early events in EMT.